I am PhD candidate (ABD) in political science at UIUC, my research interests are in the use of computational and statistical methods to study important substantive questions related to mass political behavior and political economy in the American and comparative contexts. I have done research projects on dynamic modelling of mass partisan polarization and segregation, the contextual and temporal variation of individuals’ attitudes towards globalization, A Lasso-augmented time series alternative to the synthetic control method, bootstrap standard errors for matching estimators, meta-analysis as a binary classification problem, etc. I am on the academic job markert.

Publication

Area under the ROC Curve has the Most Consistent Evaluation for Binary Classification PLOS ONE

Abstract: The proper use of model evaluation metrics is important for model evaluation and model selection in binary classification tasks. This study investigates how consistent different metrics are at evaluating models across data of different prevalence while the relationships between different variables and the sample size are kept constant. Analyzing 156 data scenarios, 18 model evaluation metrics and five commonly used machine learning models as well as a naive random guess model, I find that evaluation metrics that are less influenced by prevalence offer more consistent evaluation of individual models and more consistent ranking of a set of models. In particular, Area Under the ROC Curve (AUC) which takes all decision thresholds into account when evaluating models has the smallest variance in evaluating individual models and smallest variance in ranking of a set of models. A close threshold analysis using all possible thresholds for all metrics further supports the hypothesis that considering all decision thresholds helps reduce the variance in model evaluation with respect to prevalence change in data. The results have significant implications for model evaluation and model selection in binary classification tasks. (Link)

Selected Working Paper Abstracts

Paper 1: A computational model of the geography of mass partisan polarization (Under Review)

Abstract: Both partisan polarization and partisan segregation are phenomena of great concern in the US in recent decades. Existing research that looks at mass partisan polarization and geography in conjunction focuses exclusively on the extent of partisan geographical sorting but neglects the substantial effect geographical context can have on the formation of mass partisan polarization. This paper shows that while partisan geographical sorting can happen to a certain extent, it only happens in spaces where there is a certain level of partisan segregation to start with. In essence, geographical context’s effect on partisan polarization is more substantial than partisanship’ effect on geographical sorting, a surprising result given current literature’s focus on the latter rather than the former.

Paper 2: Lasso for counterfactual prediction with artificial control (Under Review)

Abstract: The synthetic control method is increasingly being used to estimate the effect of an intervention when units of interest are at the aggregate level and there does not exist suitable control units to serve as the counterfactual for the treated unit. However, it can suffer from the problem of unsatisfactory model fit when the artificially constructed synthetic control does not approximate well the pre-intervention outcome trajectory of the treated unit. More specifically, sparsity of the control units that have predictive power for outcome of the treated unit is not sufficiently explored in the literature. Here, we show that a sparsity estimator and Lasso (least absolute shrinkage and selection operator) in particular can not only effectively select control units that are important predictors of the outcome trajectory of the treated unit but also consistently estimate weights for the control units and cross-sectional covariates across different data samples. We showcase these points through simulated data and two empirical examples: effect of Right-to-work legislation on occupational fatal injury rate in the US and economic effect of Hong Kong’s political and economic integration with mainland China.